IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i4d10.1007_s00180-025-01600-x.html
   My bibliography  Save this article

The home advantage and COVID-19: the crowd support effect on the english football premier league and the championship

Author

Listed:
  • Johan Lyhagen

    (Uppsala University)

Abstract

It is well known that there is an home advantage in football (American English: soccer) where the home team wins in about 45% of the games compared to the 27% of the away team. This has mainly been attributed to the support of the home audience and is commonly denoted the crowd support effect. The COVID-19 pandemic forced many football leagues to play the games without spectators thus making it possible to analyse the effect of crowd support in football. We analyse more than 18,000 games in the two top English football leagues during the period 2001–2020 for the Premier league and the Championship with an ordinal logistic model with explanatory variables (e.g., previous team performance, a time trend, league dummy) including a pandemic dummy. We discovered that the absence of spectators has no impact on the outcome probability in the Premier League. However, it significantly reduces the probability of the home team wins in the Championship.

Suggested Citation

  • Johan Lyhagen, 2025. "The home advantage and COVID-19: the crowd support effect on the english football premier league and the championship," Computational Statistics, Springer, vol. 40(4), pages 1919-1932, April.
  • Handle: RePEc:spr:compst:v:40:y:2025:i:4:d:10.1007_s00180-025-01600-x
    DOI: 10.1007/s00180-025-01600-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-025-01600-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s00180-025-01600-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Luke S. Benz & Michael J. Lopez, 2023. "Estimating the change in soccer’s home advantage during the Covid-19 pandemic using bivariate Poisson regression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 205-232, March.
    2. Bäker Agnes & Mechtel Mario & Vetter Karin, 2012. "Beating thy Neighbor: Derby Effects in German Professional Soccer," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(3), pages 224-246, June.
    3. Leitner, Christoph & Zeileis, Achim & Hornik, Kurt, 2010. "Forecasting sports tournaments by ratings of (prob)abilities: A comparison for the EUROÂ 2008," International Journal of Forecasting, Elsevier, vol. 26(3), pages 471-481, July.
    4. Zeileis, Achim, 2006. "Object-oriented Computation of Sandwich Estimators," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 16(i09).
    5. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
    6. Kai Fischer & Justus Haucap, 2021. "Does Crowd Support Drive the Home Advantage in Professional Football? Evidence from German Ghost Games during the COVID-19 Pandemic," Journal of Sports Economics, , vol. 22(8), pages 982-1008, December.
    7. Goddard, John, 2005. "Regression models for forecasting goals and match results in association football," International Journal of Forecasting, Elsevier, vol. 21(2), pages 331-340.
    8. Hvattum, Lars Magnus & Arntzen, Halvard, 2010. "Using ELO ratings for match result prediction in association football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 460-470, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. da Costa, Igor Barbosa & Marinho, Leandro Balby & Pires, Carlos Eduardo Santos, 2022. "Forecasting football results and exploiting betting markets: The case of “both teams to score”," International Journal of Forecasting, Elsevier, vol. 38(3), pages 895-909.
    2. Chowdhury, Subhasish M. & Jewell, Sarah & Singleton, Carl, 2024. "Can awareness reduce (and reverse) identity-driven bias in judgement? Evidence from international cricket," Journal of Economic Behavior & Organization, Elsevier, vol. 226(C).
    3. J. James Reade & Dominik Schreyer & Carl Singleton, 2022. "Eliminating supportive crowds reduces referee bias," Economic Inquiry, Western Economic Association International, vol. 60(3), pages 1416-1436, July.
    4. Wheatcroft, Edward, 2020. "A profitable model for predicting the over/under market in football," LSE Research Online Documents on Economics 103712, London School of Economics and Political Science, LSE Library.
    5. Szczecinski Leszek, 2022. "G-Elo: generalization of the Elo algorithm by modeling the discretized margin of victory," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 18(1), pages 1-14, March.
    6. Wheatcroft, Edward, 2020. "A profitable model for predicting the over/under market in football," International Journal of Forecasting, Elsevier, vol. 36(3), pages 916-932.
    7. Lasek, Jan & Gagolewski, Marek, 2021. "Interpretable sports team rating models based on the gradient descent algorithm," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1061-1071.
    8. Koopman, Siem Jan & Lit, Rutger, 2019. "Forecasting football match results in national league competitions using score-driven time series models," International Journal of Forecasting, Elsevier, vol. 35(2), pages 797-809.
    9. P. Gorgi & S. J. Koopman & R. Lit, 2023. "Estimation of final standings in football competitions with a premature ending: the case of COVID-19," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 233-250, March.
    10. J. James Reade & Carl Singleton & Alasdair Brown, 2021. "Evaluating strange forecasts: The curious case of football match scorelines," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(2), pages 261-285, May.
    11. Gross, Johannes & Rebeggiani, Luca, 2018. "Chance or Ability? The Efficiency of the Football Betting Market Revisited," MPRA Paper 87230, University Library of Munich, Germany.
    12. Alejandro Álvarez & Alejandro Cataldo & Guillermo Durán & Manuel Durán & Pablo Galaz & Iván Monardo & Denis Sauré, 2025. "Data science approach to simulating the FIFA World Cup Qatar 2022 at a website in tribute to Maradona," Computational Statistics, Springer, vol. 40(4), pages 2223-2247, April.
    13. Andreas Heuer & Oliver Rubner, 2014. "Optimizing the Prediction Process: From Statistical Concepts to the Case Study of Soccer," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-9, September.
    14. Marc Garnica-Caparrós & Daniel Memmert & Fabian Wunderlich, 2022. "Artificial data in sports forecasting: a simulation framework for analysing predictive models in sports," Information Systems and e-Business Management, Springer, vol. 20(3), pages 551-580, September.
    15. J. James Reade & Sachiko Akie, 2013. "Using Forecasting to Detect Corruption in International Football," Working Papers 2013-005, The George Washington University, The Center for Economic Research.
    16. Constantinou Anthony Costa & Fenton Norman Elliott, 2013. "Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(1), pages 37-50, March.
    17. Thorsten Schank & Vivien Voigt & Christian Orthey, 2024. "During and after COVID-19: What happened to the home advantage in Germany's first football division?," Papers 2411.12509, arXiv.org.
    18. Schwarz Wolf, 2012. "Predicting the Maximum Lead from Final Scores in Basketball: A Diffusion Model," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(4), pages 1-15, November.
    19. Csató, László, 2023. "How to avoid uncompetitive games? The importance of tie-breaking rules," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1260-1269.
    20. Baboota, Rahul & Kaur, Harleen, 2019. "Predictive analysis and modelling football results using machine learning approach for English Premier League," International Journal of Forecasting, Elsevier, vol. 35(2), pages 741-755.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:40:y:2025:i:4:d:10.1007_s00180-025-01600-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.